Predictive analysis support of remote tracking

ABSTRACT

A method for predictive analysis for maintaining a battery charge of a personal monitoring device is provided. The method includes: locking the personal monitoring device to a limb of a monitored person; collecting historical information about the personal monitoring device including movement data of the personal monitoring device and battery charging history of the personal monitoring device; analyzing the collected historical information for patterns of battery charging behavior; receiving a current location of the personal monitoring device; determining whether to send a battery charge reminder to the monitored person, based on at least a current location of the personal monitoring device, a current batter charge, and the patterns of battery charging behavior; and providing, in response to a positive outcome of the determining, a notification to the monitored person to charge the battery.

FIELD OF THE INVENTION

Various embodiments described herein relate generally to enhancedtracking of monitored devices. More particularly, various embodimentsherein relate to enhanced tracking of monitored devices using machinelearning techniques in an effort to favorably affect a positive outcomeof a monitored person's completion of a monitoring program.

BACKGROUND

The monitoring of movements of monitored individuals involves a varietyof sectors, including parolees and home confinement. The technology hasits roots in the home arrest systems of the 1980's, in which a userwearable component—typically a “beacon” anklet that was locked to themonitored individual—would communicate wirelessly with a stationary baseunit. The range was limited to a few feet of the radio frequencytransmitter and receiver. The base unit included a telephone connectionfor communicating with the authorities. If the monitored individual leftthe short range allowed by the equipment, the tag and the base unitwould lose contact and the base unit would respond by sending an alertto the authorities. False alarms for minor deviations from the shortrange and/or an inability to confirm false from actual alarms (if theperson was where they were supposed to be when the police arrived toinvestigate) desensitized the police to such alerts, rendering thetechnology of limited application to low risk offenders.

A later generation of the technology incorporated GPS and cellulartelephone technology in a locked anklet. The device would activelyrecord the location of the monitored individual over time and transmitthe data to a central monitoring (e.g., police or parole monitoringservices). The central monitoring could store and analyze the data forprohibited movements (e.g., a sex offender near a school) or crossreference the movement data with crime incident data to see if themonitored individual was near the crime at the time of the crime. U.S.Pat. Nos. 5,867,103, 6,160,481, 6,218,945, 6,512,456 and 6,703,936,incorporated herein by reference in their entireties, are each exemplaryof such a system. The technology is also used to monitor otherindividual sectors (e.g., patients, children) and objects (e.g., cars,cargo).

The modern monitoring device includes a GPS receiver that determineslocation, a memory that stores location data over time to define amovement history, and a cellular modem that communicates the movementhistory to a central location through the cell network; some of thesedevices can also monitor for the presence or consumption of narcotics. Acommon implementation is in the criminal justice system as analternative to incarceration, and monitored individuals (typicallyparolees) have tamper resistant devices attached to their leg by a band.For ease of discussion embodiments herein are directed to parolees,although the invention is not so limited.

The ultimate goal of a parolee monitoring program is the successfulcompletion of the program so a former parolee can reintegrate intosociety as a free citizen. A primary source of failure to complete theprogram is violations of the program terms that generate various levelof alerts that require investigation and/or response by supervisingauthorities. Non-limiting examples of such violations include tamperingwith the monitoring device, failing to charge the battery of themonitoring device, entering an exclusion zone, leaving or later arrivalto an inclusions zone, consuming prohibited substances, etc. Suchviolations will be detected by the monitoring device and/or the centralmonitoring and identified for follow up by authorities (typically thepolice or parole officers). One or more of these violations can resultin the loss of parolee and return to incarceration.

Assessment of the consequences of violations is left to the discretionof the supervising authority, in particular the probation officersoverseeing any particular group of parolees. There are a variety oflimitations on the effectiveness of that oversight. One such limitationis manpower, as parolee officers may have more parolees that they canreasonably handle; limitations on available time may simply limit thenumber of issues that they can investigate. Another limitation isindividual judgment, in that one parolee officer may view a particularviolation as minor while another view the same event as significant(e.g., a parolee arrives one minute late to an inclusion zone, for whichone parolee officer could let it slide while another could return theparolee to prison). Yet another limitation is any particular paroleofficer's threshold to sensitivity to the number of violations, asinundating with alerts can become “numb” to the alerts thereby adverselyimpacting the success of their EM program as a whole.

Historically platforms that support such tracking are reactive. Theplatforms issue alerts for violations, and agent deals with these alertsaccordingly. Some agents and agencies are better than others withexecution of their programs. It also should be noted that other agenciesinundated with alerts can become “numb” to the alerts thereby adverselyimpacting the success of their EM program as a whole.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments in accordance with the present disclosure will bedescribed with reference to the drawings, in which:

FIG. 1 illustrates an embodiment of a monitoring device attached to anankle of a user.

FIG. 2 illustrates an embodiment of the internal components of themonitoring device of FIG. 1 .

FIG. 3 illustrates an operating environment of the monitoring device ofFIG. 1 .

FIG. 4 illustrates a flowchart to determine whether a monitored personshould charge the battery of their personal monitoring device.

FIG. 5 illustrates a flowchart to determine whether a power outage mayhave contributed to a battery violation.

FIG. 6 illustrates a flowchart to determine whether a power outage maycontribute to a future battery violation.

FIG. 7 illustrates a flowchart to determine whether traffic may havecontributed to a zone violation.

FIG. 8 illustrates a flowchart to direct a mobile user of a tracking appto switch to the desktop version.

FIG. 9 illustrates a prediction engine and various sources ofinformation for the prediction engine.

FIG. 10 illustrates a flowchart of machine learning that may be used inthe prediction engine

FIG. 11 illustrates a logical arrangement of a set of general componentsof an example computing device that can be utilized in accordance withvarious embodiments.

DETAILED DESCRIPTION

In the following description, various embodiments will be illustrated byway of example and not by way of limitation in the figures of theaccompanying drawings. References to various embodiments in thisdisclosure are not necessarily to the same embodiment, and suchreferences mean at least one. While specific implementations and otherdetails are discussed, it is to be understood that this is done forillustrative purposes only. An individual skilled in the relevant artwill recognize that other components and configurations may be usedwithout departing from the scope and spirit of the claimed subjectmatter.

Embodiments of the instant application focus on providing a suite ofadditional operations in the monitoring devices and central monitoringto improve the likelihood of paroles successfully graduating from theprogram by reducing the number of violations, or identifying mitigatingfactors for violations. Other embodiments of the instant applicationreduce the resource demands on the supervising authority.

Several definitions that apply throughout this disclosure will now bepresented. The term “substantially” is defined to be essentiallyconforming to the particular dimension, shape, or other feature that theterm modifies, such that the component need not be exact. For example,“substantially cylindrical” means that the object resembles a cylinder,but can have one or more deviations from a true cylinder. The term“comprising” when utilized, means “including, but not necessarilylimited to”; it specifically indicates open-ended inclusion ormembership in the so-described combination, group, series and the like.The term “a” means “one or more” absent express indication that it islimited to the singular. “First,” “second,” etc. are labels todifferentiate like terms from each other, and does not imply any orderor numerical limitation.

The phrase “monitored population” refers to a group of individualsand/or objects that have issued monitoring devices and are subject tocommon electronic location monitoring by an oversight system. Thephrases “monitored individual,” “monitored person” or the like refers toa particular individual in the monitored population.

An “authorized individual”, “supervising authority” or the like would beany one or more people having some type of supervisory responsibilityand/or decision making relative to the monitored population or monitoredindividual therein. By way of non-limiting example, a parole officerwould be the authorized individual relative to a parolee. Othernon-limiting examples include parent/child, patient/medical supervisor,etc. Security and/or police could be considered a supervising authorityrelative to a population of monitored individuals to the extent theemergency level intervention is considered necessary. For sake ofbrevity, discussion herein is confined to parolee/parole officerrelationship, although the invention is not so limited. The inventionalso applies to objects that can be monitored, such as cars or cargo.

In the concept of technologically determining a geographic position of aperson or object, the term “location” refers to the position that thetechnology identifies the person or object to be, as opposed to theactual physical location of the person or object. Such trackingtechnologies have some degree of margin of error such that anytechnologically determined location will not be an exact location of theperson or object. For example, under certain conditions GPS has a knownmargin of error within 1-5 meters, and thus the location determined fora person using GPS under those conditions may differ from their actualposition by 1-5 meters. Any such technological determinations ofposition are thus approximate based on the underlying accuracy of thetracking technology and margin of error. Any use of “location” in thespecification or claims is to be considered modified by “approximate” toaccount for such variation whether expressly stated or not.

“Inclusion zone” refers to a bounded geographic area in which amonitored person is expected to be. An inclusion zone may have anassociated time frame, in that the monitored person is expected to bewithin that inclusion zone during specific hours (e.g., at home from 12AM-7 PM, at work from 9 AM-5 PM). As used herein, references tolocations in which the monitored person is expected to be such as“work”, “home” or the like refer to inclusion zones.

“Inclusion zone violation” refers to the monitored device identifyingthe location of the parolee as outside an inclusion zone during a timeframe in which the parolee is expected to be within the inclusion zone.

“Exclusion zone” refers to a bounded geographic area in which amonitored person may not enter. An exclusion zone may have an associatedtime frame, in that the monitored person may not enter an exclusion zoneduring specific hours (e.g., a school from 7 AM-5 PM). As used herein,references to locations in which the monitored person is excluded fromsuch as “school”, “bar” or the like refer to exclusion zones.

“Exclusion zone violation” refers to the monitored device identifyingthe location of the parolee as inside an exclusion zone during a timeframe in which the parolee is expected to be outsize the exclusion zone.

“Battery violation” refers to a threshold level of battery charge thatthe parolee must maintain to comply with the monitoring program, suchthat depletion of the battery charge below that level violates theprogram.

Tracking Devices

FIG. 1 shows a block diagram of a personal monitoring device 102according to an embodiment of the invention. Personal monitoring device102 can determine its location, such as through the Global PositioningSatellite, cell towers, LORAN, wireless local access points, or otherknown methodologies; for sake of brevity discussion herein is confinedto GPS, although the invention is not so limited. Monitoring device 102is shown as a one-piece unit, although multiple pieces as known in theart could also be used.

A band 104 secures monitoring device 102 to a limb of the user,typically the ankle, via a locking mechanism that preferably can only be(legally) opened by an authorized individual. An ankle is shown in FIG.1 , although the invention is not limited thereto. Monitoring device 102and band 104 preferably have tamper detection capabilities as is knownin the art and not discussed further herein. The invention is notlimited to any particular securing and/or tamper detection methodology.

Referring now to FIG. 2 , a non-limiting example of monitoring device102 includes a dedicated circuit with hardware and/or software fordetermining its location, such as a GPS receiver 202. A cellular/longrange modem 204 preferably provided with a SIM card allows themonitoring device 102 to communicate through the cellular network with acentral monitoring 310 (FIG. 3 ) and/or an authorized person 312. Ashort range modem 206 (e.g., 915 MHz or 802.11 compatible Wi-Fi orBluetooth) allows the monitoring device 102 to communicate locally withother wireless devices within the short range as is known in the art forsuch devices. Modems 204 and 206 may be the same modem operable tocommunicate on different frequencies. A substance detector 208 may beprovided to monitor the presence of prohibited substances in themonitored individual, such as through a sensor in contact with the skin;the configuration of substance detectors within a portable monitoreddevice is known by those of skill in the art and not further discussedherein. Tamper detection mechanism 210 monitors for tamper detection asis known in the art. An accelerometer 212 may measure speed of movement.

Monitoring device 102 may also include a wireless access point detector220 that detects short range (e.g., maximum range on order of less thanhundreds of feet) wireless access points (or “WAP”) within range andrecords information about the detected wireless access points in thememory. By way of non-limiting example, wireless access points may beIEEE 802.11 compatible Wi-Fi hotspots that provide Internet access tonetwork devices as are commonly known in the art of mobile phones andtablets, and the wireless access point detector 220 may be an IEEE802.11 compatible Wi-Fi finder. In another non-limiting example, devicesthat communicate via Bluetooth could be wireless access points. Acellular tower is a long range point of access (maximum range on theorder of miles) and would not be a short range wireless access point asunderstood herein.

Batteries, a power port, memory, I/O interface, wired communicationaccess and other supporting computer hardware and software as is knownin the art are also preferably provided and not discussed furtherherein. Monitoring device 102 may be integrated into a single unit ormultiple units as is known in the art. The invention is not limited tothe details of the architecture of monitoring device 102.

The discussion herein primarily focuses on GPS signals and equipment forlocation purposes, although the invention is not so limited and otherforms of location can be used. Non-limiting examples include GLONASS,Galileo, and GAGAN. Combinations of different location methodologiescould also be used. The invention is not limited to the type of locationmethodology.

The discussion herein primarily focuses on Wi-Fi or Bluetooth signalsand equipment for short range communications, although the invention isnot so limited and other forms of short-range communication can be used.Non-limiting examples include LiFi, ZigBee, and induction wireless.Combinations of different short-range methodologies could also be used.The invention is not limited to the type of short-range communications.

The discussion herein primarily focuses on cellular and equipment forlong range communications, although the invention is not so limited andother forms of long-range communication can be used. A non-limitingexample is LoRa. Combinations of different location methodologies couldalso be used. The invention is not limited to the type of long-rangecommunications.

Referring now to FIG. 3 , monitoring device 102 is shown in an operatingenvironment. Multiple satellites 302 provide the GPS timestamps that GPSreceiver 202 in monitoring device 102 converts into locationinformation. The location information represents the approximateposition of the monitoring device 102, and by extension the approximateposition for the monitored individual, at a particular time. Monitoringdevice 102 can transmit that information as location data in near realtime, and/or can store the location information as location data inmemory and batch transfer collected location data as dictated by theneed of the system (e.g., on a fixed and/or random schedule, or inresponse to an event or specific instruction to do so). If substancedetection capability is present, then information relating toscreening(s) may be similarly stored, transmitted in near real time orbatch transmitted collectively, either with or separately from thelocation data.

Monitoring device 102 preferably has at least two options for remotecommunications, including data transfer. The first is through theshort-range modem 206 with a trusted home monitoring device 304 whenwithin the range (e.g., <300 feet, more particularly about 50-100 feet)of the short-range modem 206. The second is through the cellular/longrange modem 204 (potentially miles) to a cell tower 306 when themonitoring device is out of range of the trusted home monitoring device304. A third option is short range modem 206 with a wireless accesspoint 308. Preferably cell tower 306, wireless access point 308 and/ortrusted home monitoring device 304 connect to central monitoring 310and/or an authorized person 312, whether directly or through interveningequipment (e.g. cell network, Internet) as known in the art.

Wireless access point detector 220 is shown in FIG. 2 as separate frommodems 204 and 206, but may be part of those modems or other components.By way of non-limiting example, modem 204 could be a cellular modem,modem 206 could be a separate RF modem, and wireless access pointdetector 220 could be a distinct Wi-Fi modem and/or Wi-Fi findercomponent. In yet another example, modem 206 is a Wi-Fi network adaptorthat includes Wi-Fi finder hardware and/or software, such that thewireless access point detector 220 is part of modem 206. In still yetanother example, modem 204 could be a cellular modem, modem 206 could bea separate RF modem, and wireless access point detector 220 could be adistinct Wi-Fi network adaptor. The invention is not limited to theparticular organization or components that define the wireless accesspoint detector 220.

Central monitoring 310 is “central” in the sense that it serves one ormore monitoring devices 102. It may be a single location, multiplelocations operating independently, or a distributed arrangement. At amost basic level the central monitoring 310 is no more than a computer(e.g., a server) having a memory, processor, modem, input/output, andother supporting computer hardware and software as is known in the art,although in practice they may be large facilities with distributedcomputers and human handlers. Functionality attributed herein to centralmonitoring 310 is preferably implemented by software programmed ontoelectronic computer hardware. The invention is not limited to thearchitecture or layout of the central monitoring 310.

Predictive Engine

As noted above, embodiments of the invention are directed to a suite oftools that can improve the ability of parolees to graduate from themonitoring program. Such tools may be based on a “generic” individual,in that the tools operate on the basis that all individuals are equaland are treated the same way. Referring now to FIG. 8 , operations canbe fine-tuned or tailored to particular individuals via a predictiveengine 900 may be used to generate predictions on how a specificindividual may act under certain circumstances. Such considerations maybe based on specific past behavior of the individual, past behaviors ofsimilar classes of individuals, or both. Predictive engine 900 is acombination of electronic computer hardware programmed with software asdescribed in more detail below.

FIG. 9 shows a non-limiting example of potential sources of informationfor the predictive engine. Enrollees data features 902 represents thecharacteristics for each monitored person in the monitored population,such as race, age, gender, weight, height, home address, work address,prior criminal history, prior status as a parolee, prior violations inthe program, and/or the effect of prior reminders in the program. Thisinformation may be obtained when the monitored person enters themonitored program and/or be updated over time (e.g., they age, move,change jobs, experience violations, etc.).

Data regarding the monitoring devices 102 of the monitored populationmay also be used. This could be the raw information from the monitoringdevices 102, or from subsequent analysis as provided directly by thecentral monitoring 310. Non-limiting examples of raw information includebattery charge, location, and accelerometer data. Non-limiting examplesof processed data may include battery violations, overall battery health(whether monitoring device 102 is holding its charge), inclusion zoneviolations, and/or exclusion zone violations.

Relevant information may also include geo-time spatial events regardingbehavior of the monitored person and the surrounding environment. By wayof non-limiting example for a monitored person, the location data mayshow that the monitored person attends church services, AlcoholicsAnonymous meetings, or classroom education (e.g., college class, tradeschool). By way of non-limiting example for a surrounding environment,an area around the monitored person's home or work may be a high crimeor low crime area as reflected by recorded crimes at specific times andlocations.

Relevant information may also be obtained from external sources notdirectly related to the tracking architecture.

For example, if traffic prevents a monitored person from reaching aninclusion zone at a designated time, independent sources of trafficinformation 904 (e.g., GOOGLE MAPS) may provide relevant information toassess fault for the violations. Similarly, if road closures redirecttraffic out of an inclusion zone and/or into an exclusion zone, thensources of such traffic information 904 may provide relevant informationto assess fault for the violation.

Another source of information 906 may be the location of establishmentsthat sell liquor, such as bars and liquor stores. To the extent thatmonitored persons are prohibited from purchasing or consuming alcohol,the known location of such establishments from source of information 906may be relevant for predictive engine 900. To the extent that it isavailable, open hours of such establishments may also be of value, asproximity of a monitored person to such an establishment when it isclosed is not likely to constitute improper activity.

Yet another source of information 908 is the availability of utilitypower, and corresponding location and time of any power outages. Suchinformation may be relevant if the absence of utility power wasresponsible for a battery violation because the monitored person couldnot charge their monitoring device 102.

Yet another source of information 910 is the criminal activity in thearea of the monitored person, such information being provided by theauthorities 312 or from central monitoring 310. While the monitoredperson may have no direct relation to any specific criminal activity,the fact that the monitored person is proximate to a high crime ratearea as opposed to a low crime rate area may be a contributing factor tosuccessful completion of the program.

The above sources of information are only non-limiting examples. Othersources and combinations of source can also be used based on what isavailable and desirable.

Predictive engine 900 may operate using machine learning principles, andparticularly by considering how different combinations of factorsimprove or detract from successful program compliance. Referring now toFIG. 10 , a flowchart 1000 of an example of a process for generating andusing a machine-learning model according to some aspects.

In block 1002, training data is received. In at least some embodimentsherein, training data may include personal characteristics of themonitored population (e.g., characteristics for each monitored person inthe monitored population, such as race, age, gender, weight, height,home address, work address, prior criminal history, prior status as aparolee, prior violations in the program, and/or the effect of priorreminders in the program), prior behaviors, movement data, batterycharging history, violation history, etc. In some examples, the trainingdata is received from a remote database or a local database, constructedfrom various subsets of data, or input by a user. The training data canbe used in its raw form for training a machine-learning model orpre-processed into another form, which can then be used for training themachine-learning model. For example, the raw form of the training datacan be smoothed, truncated, aggregated, clustered, or otherwisemanipulated into another form, which can then be used for training themachine-learning model.

In block 1004, a machine-learning model is trained using the trainingdata. In at least some embodiments herein, the machine-learning modelcan be trained in a supervised manner, in which each input in thetraining data is correlated to a particular output. This particularoutput may be a scalar, a vector, or a different type of data structuresuch as text or an image. This may enable the machine-learning model tolearn a mapping between the inputs and desired outputs. However, theinvention is not so limited, and training may be unsupervised (thetraining data includes inputs, but not particular outputs, so that themachine-learning model has to find structure in the inputs on its own)or semi-supervised training (only some of the inputs in the trainingdata are correlated to particular outputs).

In block 1006, the machine-learning model is evaluated for accuracy. Forexample, an evaluation dataset can be obtained, for example, via userinput or from a database. The evaluation dataset can include inputscorrelated to desired outputs. The inputs can be provided to themachine-learning model, and the outputs from the machine-learning modelcan be compared to the desired outputs. In at least some embodiments,the outputs of the predicted activity of portions of the monitoredpopulation could be compared with the actual activity of portions of themonitored population.

If the outputs from the machine-learning model closely correspond withthe desired outputs, the machine-learning model may have a high degreeof accuracy. For example, if 90% or more of the outputs from themachine-learning model are the same as the desired outputs in theevaluation dataset, the machine-learning model may have a high degree ofaccuracy. Otherwise, the machine-learning model may have a low degree ofaccuracy. The 90% number is an example only. A realistic and desirableaccuracy percentage is dependent on the problem and the data.

In some examples, if the machine-learning model has an inadequate degreeof accuracy for a particular task, the process can return to block 1004,where the machine-learning model can be further trained using additionaltraining data or otherwise modified to improve accuracy. If themachine-learning model has an adequate degree of accuracy for theparticular task, the process can continue to block 1008.

Steps 1002-1006 may loop, in that the methodology may receive newtraining data over time and repeatedly update the model to account fornew training data.

In block 1008, new data is received. In some examples, the new data isreceived from a remote database or a local database, constructed fromvarious subsets of data, or input by a user. The new data may be unknownto the machine-learning model. For example, the machine-learning modelmay not have previously processed or analyzed the new data. In at leastsome embodiments herein, new data may be, for a current location of apersonal monitoring device 102, current battery charge, currentavailability of utility power, and/or current traffic conditions.

In block 1010, the trained machine-learning model is used to analyze thenew data and provide a result. For example, the new data can be providedas input to the trained machine-learning model. The trainedmachine-learning model can analyze the new data and provide a resultthat includes a classification of the new data into a particular class,a clustering of the new data into a particular group, a prediction basedon the new data, or any combination of these.

In block 1012, the result is post-processed. For example, the result canbe added to, multiplied with, or otherwise combined with other data aspart of a job. As another example, the result can be transformed from afirst format, such as a time series format, into another format, such asa count series format. Any number and combination of operations can beperformed on the result during post-processing.

Monitoring systems typically work on a “stick” mentality, in thatenrollee compliance is based on fear of punishment for programviolations. While embodiments herein can be similarly used, they can beused for a “carrot” approach of rewards for successful compliance withthe program. Predictive engine 900 can be particularly valuable for thelatter, for just as it can predict actions that (historically) lead tofailure, it can also predict actions that can lead to success.

Battery Charging

A particular aspect of GPS monitoring that contributes to paroleefailure is not consistently charging the battery of monitoring device102. If the battery charge falls below a particular level then themonitoring device 102 will be unable to track the monitored person.Therefore under most parolee requirements it is the parolee'sresponsibility to keep the battery of monitored device 102 charged abovea certain power level, and it is a program violation if they fail to doso. Dealing with a resulting battery violation places yet another burdenon a parolee officer or other supervisory agent and can contribute tothe parolee being removed from the program and returned toincarceration.

With current technology, it requires about one hour every day tomaintain a consistent battery charge for monitoring device 102. Sincemonitoring device 102 is locked onto a limb of the monitored individual,the battery charging requirement essentially requires that the monitoredindividual remain tethered to a wall outlet or other suitable powersource for one hour a day. The tethering may be physical, such asphysical cord or proximity via induction charging. The tethering mayalso be data range, such as for a two-piece monitoring device with acuff locked onto a monitored person and the tracking componentsconnected to the wall outlet such that the cuff must remain within arange of communication between the cuff and tracking components.

While maintaining an appropriate charging schedule and/or responding tobattery warnings would seem easily doable in theory, as a practicalmatter there are frequent breakdowns. This is observable daily withinthe general population by the sheer number of people whose phones diebecause they forget to charge them.

In the prior art there are a variety of methods to remind the parolee tocharge their monitored device 102. A basic approach is a low and/orcritical battery warning alert from the device itself. Another approachis for monitoring device 102 to report its battery status to centralmonitoring 310 and/or authorized person 312, which/who sends a reminderto the parolee to charge monitoring device 102. Yet another approach isa basic daily alarm reminder for a set time.

Such prior art approaches are of limited effectiveness because themonitored person may not be in a situation in which charging isavailable or a viable option. For example, if the parolee is about totravel from home to a parole officer meeting and receives a low batterywarning, then the parolee is placed in Catch-22 of either a batteryviolation for their battery dying on their way to the meeting, or ameeting violation for staying at home to charge their battery.

An embodiment of the invention provides a more intelligent system ofmonitoring parolee activity and tailoring charging instructions based onthat prior activity relative to current events. This process willidentify optimal opportunities for the parolee to charge their deviceand send corresponding reminders for the same. This process willdecrease the number of battery violations with a corresponding decreasein burden on the supervising authority, all while increasing theprobability that the parole will successfully complete their program.

Referring now to FIG. 4 flowchart 400 shows a non-limiting example ofthis methodology. FIG. 4 is generally directed to the accumulation ofinformation to identify the most optimal times for charging monitoringdevice 102. At step 402, personal historical information for a monitoredperson is collected. As discussed in more detail below, such personalinformation may be used in a predictive analysis process to furtherenhance selection of the optimum time for charging.

At step 404, device historical information for the monitored person'spersonal monitoring device 102 is collected. As is known in the art,such historical information may include movement data that reflects theapproximate location of the monitored individual over time. Theinformation may also include past battery status information. Batterystatus information could include when the battery was last charged, theamount of remaining charge at particular times, low battery conditions,and/or critical battery conditions. Step 402 and 404 may occurseparately or concurrently.

At step 406, central monitoring 310 and/or monitoring device 102 analyzethe collected information from steps 402 and 404 to identify patterns ofbehavior as it pertains to charging the battery of monitoring device102. By way of non-limiting example, the system may determine that themonitored person has a high success rate (e.g., the battery isconsistently properly charged) when the monitored person is home andcharges their device between 8-10 PM in the evening. In contrast, theremay be a lower success rate (e.g., low and/or critical battery warninglevels) when the monitored person may have a lower success rate whenthey are at another location from 8-10 PM, or if they are at home butcharge after midnight (e.g., the plug the monitoring device 102 into awall outlet charge, but fall asleep and pull the cord from the wall).The analysis may go to a deeper level, such as determining that themonitored person tends to be home Monday thru Friday and reliablycharges between 8-10 PM, but the monitored person tends to be elsewhereon Saturday and Sunday without reliably charging such that violationsare more likely to occur.

At step 408, current information is received from personal monitoringdevice 102. Such current information may include location informationand battery status information. At step 410, central monitoring 310analyzes current information relative to the determined patterns from406 to determine at step 412 whether to send the monitored person aninstruction to charge their battery. This analysis looks at the person'scurrent location and battery status relative to the patterns of behaviorto identify potential for successful and unsuccessful outcomes andreacts accordingly.

By way of non-limiting example, the pattern of behavior from step 406shows successful charging between 8-10 PM when the monitored person isat home. If the person is at home during that time, then the currentinformation from monitored device 102 at step 408 will reflect this.Step 410 thus considers the location from the current informationrelative to the pattern of behavior and determines that the monitoredperson is at a location where they are successfully charged before andthus avoided battery violations in the past. The system can react tosend, or not send, a reminder at step 412 to keep the monitored personon pace.

For example, either by successful pattern and/or reliance on thehistorical information, the system determines that it would increase ormaintain compliance for the monitored person by sending a reminder at 8PM when they are at home. In another example, the system may elect notto send a reminder at 8 PM when at home as the historical informationindicates that the monitored person is reliable (“over” notification candesensitize the monitored person), but may instead send one at 9 PM ifthe battery status at that time indicates that the monitored person hasnot charged per normal patterns.

In another example, a “carrot” approach is used. If the pattern ofbehavior shows successful charging between 8-10 PM when the monitoredperson is at home, then the methodology may determine that it wouldincrease or maintain compliance for the monitored person to send apost-charging notice at 10 PM with a positive affirmation (e.g., a textwith “Good job!”). This could be in addition to, or an alternative to,the reminder at 8 PM.

Such decision making at to what messages to send, and when, could liewith predictive engine 900, which would determine whatever actionfacilitates the highest likelihood of successful program compliance.Based on the history and current circumstances of any particularenrollee, Enrollee A may benefit most from a reminder, Enrollee B maybenefit most from a positive affirmation, and Enrollee C may benefitmost from both.

As noted above, current information from the monitoring device 102factors into the methodology by providing current location. For example,patterns of behavior may indicate that a user is expected to be home8-10 PM and make determinations when the user is at that location atthat time. However, monitored persons may not always follow predictedbehavior. If the current information shows that the user is not yet athome, then sending a reminder at that time will be of limited valuebecause the user may not be in a location to react to it. Themethodology could then take other action, such as deferring the reminderuntil the monitored person returns home as shown by (then) currentinformation from the monitoring device 102. The methodology may alsodetermine that while the user is in a different location, historicalinformation shows prior successful charging at that location at the sametime and send a reminder on that basis.

In another example, the historical information indicates that themonitored person reliably charges from Monday to Friday at 8-10 PM whenthey are home, but not on Saturday or Sunday when they are elsewhere.Reminders for Monday-Friday may be sent as above, but those samereminders may not be effective on Saturday or Sunday. In such cases, themethodology may identify other opportunities for charging. For example,there may be historical information that shows successful charging at aparticular location that the monitored individual is currently at. Eventhough it may only be earlier in the day and/or the battery still has asubstantial charge, the system may nonetheless decide to send a reminderat step 412 because the opportunity to charge is currently available andpatterns of behavior indicate a potential future charging violationunless the monitored person uses this opportunity to charge.

Battery charge level may be a considered factor in determining how thesystem should react. For example, the battery may already be near fullycharged such that charging at this time is simply not necessary, andsending a reminder may be an over notification. In another example, amonitored person unexpectedly reaches full charge in the morning at 9:30AM, such that no further charge is needed that day, for which even ifthe monitored person is home during 8-10 PM the system does not send thecustomary reminder.

Analysis at 410 may also include identifying indicia of failing batteryhealth. As is known in the art, over time batteries degrade and losetheir ability to hold charge. Typically this occurs after an expectedperiod of time and is addressed during scheduled maintenance ofmonitoring device 102. However, sometimes a battery may be defective,damaged, or just degrade faster than expected. In such circumstances,the battery may need to charge more often, and the methodology canaccordingly tailor its reminders at 412 to account for the same. Thereminder may be, or include, an instruction for the monitored person tocontact the authorities to repair or replace the battery. The failingbattery status can also be considered a mitigating or neutralizingfactor for any battery violation.

In yet another example, the monitored person may be at a location withhistorical records of successful battery charge and the current batterycharge may currently be adequate for at least short term use, yet thepredicted patterns of movement indicate that the person will leave thatlocation for a period of time and the battery would die out during thatperiod. For example, the patterns may show that the monitored personcharges successfully at home, but not at work. If the monitored personis at home with a partially charged battery and needs to leave for work,the battery may run out while the monitored person is at work. From atleast the current battery charge, the movement history, inclusionszones, and/or the battery charge history, the methodology predicts at410 that absent additional charge the battery will fall below a minimumthreshold at a time when the user has a poor track record of batterycharging. While the battery has sufficient charge at the moment, themethodology nonetheless sends a battery charge reminder notification at412 to allow the monitored person to avail themselves of power at thecurrent location and avoid a future battery violation.

The above analysis may consider the personal historical information viamachine learning techniques that study patterns of behavior based on themonitored population, which may change the reminder protocol forotherwise similar monitored individuals. The monitored person haspersonal characteristics (examples discussed above) that are common withother members of the monitored population. By way of non-limitingexample, a common characteristic is female gender, and personalhistorical information for the monitored persons may show that women aremore reliable than men in charging, such that reminders to women do nottend to improve compliance whereas reminders to men do tend improvecompliance. The consideration may also be personal, in that historicalrecords may show that reminders actually help the monitored person(e.g., they begin to charge shortly after receiving the reminder) or donot (not meaningful change in behavior). Personal historical informationalso includes past violations, and if the monitored person is actingconsistently with the reminders and there are no violations, it mayreduce or completely phase out the reminders. Likewise, it can resume orincrease the reminders if the violations increase or reoccur.

The above concepts of reliability, success and the like representpredefined parameters as set by the system administrator. By way ofnon-limiting example, battery charging at a location may be consideredsuccessful only if there has never been a battery violation while atthat location. In another example, success may allow for one violationper week. Probabilities relative to action are also predetermined. Byway of non-limiting example, the methodology may establish that anotification should be sent if there is a 20% or greater chance of apotential battery violation. The invention is not limited to whatpredefined parameters establish these concepts.

Power Outages

Other embodiments relate to monitoring and accounting for power outagesin battery charging behavior. It can be difficult if not impossible fora monitored person to charge monitoring device 120 during a poweroutage. As it is the responsibility of the monitored person to maintainan appropriate battery charge for monitored device 102, but somejurisdictions may elect not to fault the monitored person if power wasnot available to charge the battery. Accounting for the impact of poweroutages can therefore eliminate or at least reduce the severity ofbattery charge violations, thereby improving prospects for successfulcompletion of the program.

Referring now to FIG. 5 , a flowchart 500 is shown for addressing theimpact of past power outages. At step 502 the system receivesinformation that defines the physical area and/or duration of a poweroutage. Such data may be acquired directly from the local utility, or beentered by authorized personnel.

Such power outage data may also be available, or at least inferable,from the monitoring programs' own equipment. Some monitoring programsprovide monitored persons with location installed accessory equipment(such as BLUHOME as sold by Applicant) that runs off of local power. Ifpower is lost, that equipment can transfer to internal battery power andreport the power loss to the central monitoring 310. Central monitoring310 can interpret such a power loss as a power outage. As a practicalmatter loss of power for only one signal source is not conclusive (e.g.,the parolee could have just shut off power at the fuse box, but at thevery least this may be investigable), but if the problem is observedover multiple signal sources over a deployed area (e.g., all signalsources within a one mile area), then this would tend to indicate apower outage. Central authority can either accept those results orcontact the utility power company for further information.

At step 504 device historical information is received from personalmonitoring device 102. Such current information may include locationinformation and battery status information, in particular any batteryviolations. At step 506, central monitoring 310 cross references theidentified power outage with historical location information and batterystatus information to determine whether a battery violation occurredwhile the monitored person was within the scope of the power outage.

If the cross reference at step 506 indicates that the battery violationwas linked to a power outage, central monitoring 310 and/or authorizedperson 312 can take one or more reactionary steps at step 508 to addressthe same. There are a variety of possible reactions, and the inventionis not limited to a particular reaction.

A non-limiting example of a reaction may be to enter a note in themonitored person's record to that effect, so that it may be considered amitigating or neutralizing factor for the violation itself.

Another non-limiting example of a reaction may be to notify anauthorized person of the situation so that they can take any actionsthey deem appropriate.

Step 506 may be considered conclusive of the responsibility, or lackthereof, for a battery violation. However, the battery violation couldbe coincidental with a power outage for which the authorized person 312does not wish to allow it to slide. For example, if the battery wasabout to die and there was suddenly a power outage, the authorities maystill consider it the responsibility of the parolee to have chargedearlier. Step 506 could thus also include an analysis of whether theduration of the power outage meaningfully (as opposed to coincidentally)prevented charging (e.g., power was out for a full day). Step 506 couldalso consider whether, based on the methodology of FIG. 4 , the paroleedid not avail themselves of an earlier opportunity to charge thebattery. Any or all of these factors may be taken into account indeciding how to act at step 508.

The above methodology is based on past consideration of batteryviolations relative to historical data. However, the invention is not solimited, and may involve real time events. Referring now to FIG. 6 , aflowchart 600 is shown for addressing the impact of past power outages.

At step 602 the system receives information that defines the physicalarea and/or duration of a power outage. The methodology for receivingsuch information is similar to that of step 502 and is not furtherdiscussed herein.

At step 604 the system receives current device information frommonitoring device 102. At step 606 the system analyzes the ability ofthe monitored person to comply with the requirements of the monitoringprogram relative to the lack of available power. Non-limiting examplesof potential non-compliance would be an inability to charge the battery(which as noted above may be difficult or impossible), or the need totravel to a location such as work that is now closed.

At step 608 the system reacts in response to the circumstances.Non-limiting examples of reactions are discussed with respect to step508 for battery violations. Another non-limiting example of a reactionmay be to contact the monitored person with questions (e.g., “pleaseconfirm whether your home has power”), device instructions (e.g.,“connect your device to a power outlet” to allow monitored device 102 toreport that is unable to receive power) or personal instructions (e.g.,“remain at your location until further notice” or “travel immediatelythe police station”). Central monitoring 310 and/or authorized person312 could alter the monitored person's schedule to accommodate thesechanges. By way of non-limiting example, if the monitored person issupposed to leave home at 9 AM and arrive at work by LOAM, that schedulecan be revised to extend the stay at home period while canceling theappear at work requirement.

The same methodology as above could be applied to loss of signal at celltowers if cell coverage is suddenly lost, such that monitored device 102was no longer able to report information to central monitoring 310. Themethodology would track that of FIGS. 5 and 6 , save that the inquirywould be for inactive cell towers rather than power outages.

Information from predictive engine 900 may influence the nature of thereaction at step 508 or 608. Predictive engine 900 can analyzeinformation about the particular monitored person and determine whetherthe monitored person is more or less likely to take advantage of thepower outage to engage in prohibited activity that cannot be monitoreddue to the power loss. For example, the predictive engine 900 mayindicate that generally a 50-year-old woman is unlikely to engage inprohibited behavior while a 20 year old man is likely to engage in suchbehavior. Such class specific assessment may also be adjusted on apersonal basis, in that a particular 50-year-old woman with a history ofviolations may be a higher risk whereas a particular 20 year old manwith no history of violations may be a lower risk.

On-Time Arrival

Most parolee programs utilize so-called inclusion zones to define wherea monitored person is supposed to be at during certain times. Forexample, a monitored person is supposed to be at work from 9 AM-5 PM,home from 7 PM-7 AM, and gaps between allow for travel time. It is aprogram violation if current information from monitoring device 102shows that the monitored person is not within the inclusion zone duringthe designated periods.

A natural obstacle to on time arrival at a location is travelconditions, such as traffic and timeliness of public transportation. Amonitored person may typically be able to leave work and return homewithin the designated periods, but there may be occasions in whichconditions change in a manner that prevents timely arrival. If themonitored person needs to cross a bridge to get home on time and thebridge suddenly closes, or if the public transportation is delayed, itmay simple not be possible for the monitored person to get home on time.This inclusion zone violation is therefore technically a programviolation, but arguably it is not the fault the monitored person ifconditions simply prevented compliance.

Referring now to FIG. 7 , a flowchart 700 is shown. At step 702, centralmonitoring 310 accesses its own records of movement history to at step704 identify patterns in the monitored person's behavior relative totheir obligations. For example, the analysis may determine that themonitored person is due home at 7 PM, for which they have a pattern ofbehavior to leave work at 6 PM and arrive home at 6:45 PM. Themethodology may also identify variances from that typical behavior, suchas leaving later than 6 PM and/or leaving on time yet arriving homelater than 6:45 PM.

Central monitoring 310 may be particular to the tracking of themonitoring devices 102, and thus lacks access to information in otherdatabases, such as traffic and public transportation (e.g., train, bus)activity. At step 706, central monitoring reaches out to externaldatabases of third-party sources for travel information, such as GOOGLEMAPS for traffic, and/or whatever local agency is responsible for thetrains/buses schedules and delays.

At step 708, central monitoring 310 analyzes the movement patterns for amonitored person to determine the impact of travel conditions on howlong it takes the monitored person to travel between locations.Continuing with the above example, movement patterns may show themonitored person tends to arrive early at 6:45 PM under normal trafficconditions, and consistently after 7 PM (and thus a violation of theinclusion zone requirement) under heavy traffic conditions.

Two processes may flow from step 708. The first process is a historicalanalysis to identify if travel conditions (e.g., traffic, public transitdelay) were responsible for an inclusions zone violation. At step 710the methodology cross references any inclusion zone violations withunusual travel conditions that would account for the violation. Forexample, if the monitored person left work at their usual 6 PM, andmovement patterns show that they normally arrive home at 6:45 PM but onthis occasion arrived past curfew, the methodology can examine thetraffic data for any unusually traffic patterns on the monitoredperson's travel route during that period that would have inhibited orprevented curfew compliance. If such conditions are present, at step 712a note can be entered into the monitored person's record to that effect,such that the unusual traffic pattern may be considered a mitigating orneutralizing factor for the violation itself.

The second process is a real time analysis that observes the onset orpresence of traffic conditions that would interfere with programcompliance. At step 714 the methodology determines, based on movementpatterns, current location and traffic information, an optimum time forthe monitored person to transition from their current location to theinclusion zone. At step 716 the methodology sends a notice to themonitored person when they should leave their current location tomaintain program compliance.

By way of non-limiting example, if the monitored person is (a) at work,(b) movement patterns show they normally leave at 6 PM for 7 PM curfewat home and (c) traffic conditions are normal, then the system may senda notice at 5:50 PM to the monitored person that traffic is normal withreminder and they will arrive home on time if the leave by 6 PM. But iftraffic conditions are heavy or worsening, the system may send a noticeearlier, such as 5:30 PM, to the monitored person that traffic is heavy,and they need to leave by a specific earlier time to arrive home withincurfew. In yet another example, the person normally leaves at 6 PM butlocation data shows that they are still at work say at 6:10, the systemmay send a notice that they need to leave immediately else risk aninclusions zone violation.

Such messages may include opportunities for the monitored person torespond, e.g., that the monitored person is not being allowed to leavework yet. This may ultimately lead to a violation, but the circumstanceswill be documented in real time and a note can be entered into themonitored person's record to that effect and may be considered amitigating or neutralizing factor for the violation itself.

At step 718, the methodology can alter the inclusion zones to accountfor traffic changes. By way of non-limiting example, if the inclusionszone for home starts curfew at 7 PM but traffic is creating a 15-minutedelay, central monitoring 310 can adjust the curfew time to begin at7:15 PM. In another example, the inclusion zone may be a particularcorridor that the monitored person must travel (e.g., certain streets,certain highways), for which central monitoring can change the corridorto allow for alternative routes that avoid the traffic conditions.

Information from predictive engine 900 may influence the above steps.Predictive engine 900 can analyze information about the particularmonitored person and determine whether the monitored person is more orless likely to address traffic problems on their own. For example, thepredictive engine may indicate that a 20-year-old man is likely to drivefaster in a time crunch while a 50 year old woman may not. Such classspecific assessment may also be adjusted on a personal basis, in that aparticular monitored person with a history of violations may be a higherrisk whereas a different person with no history of violations may be alower risk.

Mobile Support

Parole officers often can access information about parolees througheither a desktop web portal on their personal computer or a mobile appon their mobile devices. The desktop web portal is typically the morerobust of the two, providing information beyond that which is availableon the mobile app and which is often needed for more problematicmonitored individuals. However, Applicants have observed that paroleofficers increasingly tend to use their mobile phones more than theirpersonal computers, and thus certain information may not be available tothem. This transition to reliance on the mobile app conflicts with theneed to consult with the desktop web portal for the more robustinformation.

An embodiment herein is directed to using the predictive engine 900 toanalyze both the monitored population and the parole officer to improveresponse conditions. Referring now to flowchart 800 in FIG. 8 , at step802 the methodology can categorize/stratify monitored persons based ontheir behavior into various categories to know that they are moreproblematic and need more advanced features of the desktop for review bythe authorized person 312. At step 804, the methodology analyzesauthorized person's 312 travel movements and use of the mobile anddesktop platforms. Prediction engine 900 identifies patterns at step 806in the data for circumstances that led up to the authorized personchecking the desktop app. For example, the patterns may show that theauthorized person 312 travels from home to the parolee office in theearly AM and then uses the desktop app, but does not use the desktop appat other locations or if they arrive at the office later in the day.

At step 808, the methodology determines whether to send the authorizedperson 312 a notification to check the desktop web portal and send thenotification under designated conditions. For example, if step 806predicts that the authorized person 312 will be proximate to a desktopweb portal, then the system may send a notification simply due toproximity. In another example, the notification may be sent due to acombination of the prediction and the presence of activity at step 802that requires further consultation through the web portal. In yetanother example, the methodology may withhold a scheduled remindermessage if there is no need to consult the web portal, or if theauthorized person is deviating from the predicted pattern such that senda reminder would not be valuable at that time.

The above methodology improves the agent workflow and ease of use. Italso improves the prospects for an enrollee's successful completion ofthe program because the agent is more engaged and pro-actively helpingthem to correct behavior.

FIG. 11 illustrates a logical arrangement of a set of general componentsof an example computing device 1100 as could be used for the computerelements of any electronics discussed herein, including monitoringdevice 102, predictive engine 900, etc. In this example, the deviceincludes a processor 1102 for executing instructions that can be storedin a memory device or element 1104. As would be apparent to one ofordinary skill in the art, the device can include many types of memory,data storage, or non-transitory computer-readable storage media, such asa first data storage for program instructions for execution by theprocessor 1102, a separate storage for images or data, a removablememory for sharing information with other devices, etc.

The device typically will include some type of display element 1106,such as a touch screen or liquid crystal display (LCD), although devicessuch as portable media players might convey information via other means,such as through audio speakers. As discussed, the device in manyembodiments will include at least one input element 1112 able to receiveconventional input from a user. This conventional input can include, forexample, a push button, touch pad, touch screen, keyboard, mouse,keypad, or any other such device or element whereby a user can input acommand to the device. In some embodiments, the computing device 1100 ofFIG. 11 can include one or more network interface elements 1108 forcommunicating over various networks, such as a Wi-Fi, Bluetooth, RF,wired, or wireless communication systems. The device in many embodimentscan communicate with a network, such as the Internet, and may be able tocommunicate with other such devices.

Various embodiments discussed or suggested herein can be implemented ina wide variety of operating environments, which in some cases caninclude one or more user computers, computing devices, or processingdevices which can be used to operate any of a number of applications.User or client devices can include any of a number of general-purposeindividual computers, such as desktop or laptop computers running astandard operating system, as well as cellular, wireless, and handhelddevices running mobile software and capable of supporting a number ofnetworking and messaging protocols. Such a system also can include anumber of workstations running any of a variety of commerciallyavailable operating systems and other known applications for purposessuch as development and database management. These devices also caninclude other electronic devices, such as dummy terminals, thin-clients,gaming systems, and other devices capable of communicating via anetwork.

Most embodiments utilize at least one network that would be familiar tothose skilled in the art for supporting communications using any of avariety of commercially available protocols, such as TCP/IP, OSI, FTP,UPnP, NFS, CIFS, and AppleTalk. The network can be, for example, a localarea network, a wide-area network, a virtual private network, theInternet, an intranet, an extranet, a public switched telephone network,an infrared network, a wireless network, and any combination thereof.

In embodiments where the computing device includes a Web server, the Webserver can run any of a variety of server or mid-tier applications,including HTTP servers, FTP servers, CGI servers, data servers, Javaservers, and business application servers. The server(s) also may becapable of executing programs or scripts in response requests from userdevices, such as by executing one or more Web applications that may beimplemented as one or more scripts or programs written in anyprogramming language, such as Java®, C, C# or C++, or any scriptinglanguage, such as Perl, Python, or TCL, as well as combinations thereof.The server(s) may also include database servers, including withoutlimitation those commercially available from Oracle®, Microsoft®,Sybase®, and IBM®.

The environment can include a variety of data stores and other memoryand storage media as discussed above. These can reside in a variety oflocations, such as on a storage medium local to (and/or resident in) oneor more of the computers or remote from any or all of the computersacross the network. In a particular set of embodiments, the informationmay reside in a storage-area network (“SAN”) familiar to those skilledin the art. Similarly, any necessary files for performing the functionsattributed to the computers, servers, or other network devices may bestored locally and/or remotely, as appropriate. Where a system includescomputerized devices, each such device can include hardware elementsthat may be electrically coupled via a bus, the elements including, forexample, at least one central processing unit (CPU), at least one inputdevice (e.g., a mouse, keyboard, controller, touch screen, or keypad),and at least one output device (e.g., a display device, printer, orspeaker). Such a system may also include one or more storage devices,such as disk drives, optical storage devices, and solid-state storagedevices such as random access memory (“RAM”) or read-only memory(“ROM”), as well as removable media devices, memory cards, flash cards,etc.

Such devices also can include a computer-readable storage media reader,a communications device (e.g., a modem, a network card (wireless orwired), an infrared communication device, etc.), and working memory asdescribed above. The computer-readable storage media reader can beconnected with, or configured to receive, a computer-readable storagemedium, representing remote, local, fixed, and/or removable storagedevices as well as storage media for temporarily and/or more permanentlycontaining, storing, transmitting, and retrieving computer-readableinformation. The system and various devices also typically will includea number of software applications, modules, services, or other elementslocated within at least one working memory device, including anoperating system and application programs, such as a client applicationor Web browser. It should be appreciated that alternate embodiments mayhave numerous variations from that described above. For example,customized hardware might also be used and/or particular elements mightbe implemented in hardware, software (including portable software, suchas applets), or both. Further, connection to other computing devicessuch as network input/output devices may be employed.

Storage media and computer readable media for containing code, orportions of code, can include any appropriate media known or used in theart, including storage media and communication media, such as but notlimited to volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information such as computer readable instructions, data structures,program modules, or other data, including RAM, ROM, EEPROM, flash memoryor other memory technology, CD-ROM, digital versatile disk (DVD) orother optical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium which canbe used to store the desired information and which can be accessed by asystem device. Based on the disclosure and teachings provided herein, anindividual of ordinary skill in the art will appreciate other waysand/or methods to implement the various embodiments.

The above embodiments refer to location based on GPS coordinates andcorresponding equipment to process GPS data. However, the invention isnot limited to any particular location methodology, and othermethodologies could also be used.

The specification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense. It will, however, beevident that various modifications and changes may be made thereuntowithout departing from the broader spirit and scope of the invention asset forth in the claims.

What is claimed is:
 1. A method for monitoring and maintaining batterycharge of a personal monitoring device, comprising: locking the personalmonitoring device to a limb of a monitored person, wherein the lockingrequires that the monitored person remain tethered to a power source fora period of time to charge a battery of the personal monitoring device;collecting historical information about the personal monitoring deviceincluding movement data of the personal monitoring device and batterycharging history of the personal monitoring device; analyzing thecollected historical information for patterns of battery chargingbehavior; receiving a current location of the personal monitoringdevice; first determining whether to send a battery charge reminder tothe monitored person, based on at least a current location of thepersonal monitoring device, a current battery charge, and the patternsof battery charging behavior; and providing, in response to a positiveoutcome of the determining, a notification to the monitored person tocharge the battery; wherein the first determining comprises: seconddetermining that the personal monitoring device is at a location wherethe patterns of battery charging behavior show a history of successfullybattery charging; and predicting that, absent additional charge, thebattery charge will drop below a threshold level when the personalmonitoring device is at a location where the patterns of batterycharging behavior show lack of a history of successful battery chargingat the location; wherein the first determining considers characteristicsof the monitored person relative to common characteristics or priorbehavior of the monitored population, including how those commoncharacteristics or prior behavior affect probabilities of potentialbattery violations, the characteristics includes prior criminal historyand/or prior status as a parolee.
 2. The method of claim 1, furthercomprising: collecting personal information about the monitored person;and the first determining is based on the collected personalinformation.
 3. The method of claim 2, further comprising: collectingpersonal information about a monitored population; and the firstdetermining is based on the collected personal information.
 4. Themethod of claim 1, wherein the characteristics includes any of race,age, gender, weight, height, home address, work address, prior paroleviolations, prior behavior and/or effect of prior reminders.
 5. Themethod of claim 1, further comprising: collecting personal informationabout a monitored population; and the first determining is based on thecollected personal information.
 6. The method of claim 1, wherein thefirst determining comprises: third determining that the personalmonitoring device is at a location during a time frame with a history ofsuccessfully battery charging; awaiting, for a portion of the timeframe, receipt a signal representing that the battery of the personalmonitoring device has commenced charging; the providing being inresponse to elapse of the portion of the time frame without receipt ofthe signal.
 7. The method of claim 6, further comprising: bypassing theproviding in response to receipt of the signal during the portion of thetime frame.
 8. The method of claim 1, wherein the first determining isfurther based on receipt of information indicating that the currentlocation of the personal monitoring device is in an area subject to acurrent utility power outage.
 9. A system for monitoring and maintainingbattery charge of a personal monitoring device, the system comprising:the personal monitoring device including a lockable band to lock thepersonal monitoring device to a limb of a monitored person, wherein whenlocked the monitored person must remain tethered to a power source for aperiod of time to charge a battery of the personal monitoring device;electronic computer hardware in combination with software programmed toperform operations comprising: collecting historical information aboutthe personal monitoring device including movement data of the personalmonitoring device and battery charging history of the personalmonitoring device; analyzing the collected historical information forpatterns of battery charging behavior; receiving a current location ofthe personal monitoring device; first determining whether to send abattery charge reminder to the monitored person, based on at least acurrent location of the personal monitoring device, a current batterycharge, and the patterns of battery charging behavior; and providing, inresponse to a positive outcome of the determining, a notification to themonitored person to charge the battery; wherein the first determiningcomprises: second determining that the personal monitoring device is ata location where the patterns of battery charging behavior show ahistory of successfully battery charging; and predicting that, absentadditional charge, the battery charge will drop below a threshold levelwhen the personal monitoring device is at a location where the patternsof battery charging behavior show lack of a history of successfulbattery charging at the location; wherein the first determiningconsiders characteristics of the monitored person relative to commoncharacteristics or prior behavior of the monitored population, includinghow those common characteristics or prior behavior affect probabilitiesof potential battery violations, the characteristics includes priorcriminal history and/or prior status as a parolee.
 10. The system ofclaim 9, the operations further comprising: collecting personalinformation about the monitored person; and the first determining isbased on the collected personal information.
 11. The system of claim 10,the operations further comprising: collecting personal information abouta monitored population; and the first determining is based on thecollected personal information.
 12. The system of claim 9, wherein thecharacteristics includes any of race, age, gender, weight, height, homeaddress, work address, prior parole violations, or prior behavior and/oreffect of prior reminders.
 13. The system of claim 9, the operationsfurther comprising: collecting personal information about a monitoredpopulation; and the first determining is based on the collected personalinformation.
 14. The system of claim 9, wherein the first determiningcomprises: third determining that the personal monitoring device is at alocation during a time frame with a history of successfully batterycharging; awaiting, for a portion of the time frame, receipt a signalrepresenting that the battery of the personal monitoring device hascommenced charging; the providing being in response to elapse of theportion of the time frame without receipt of the signal.
 15. The systemof claim 14, the operations further comprising: bypassing the providingin response to receipt of the signal during the portion of the timeframe.
 16. The system of claim 9, wherein the first determining isfurther based on receipt of information indicating that the currentlocation of the personal monitoring device is in an area subject to acurrent utility power outage.